Under-Review

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS).

We develop a learning framework combining offline diffusion planner and online preference alignment with weak preference labeling for legged locomotion control.

We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans.

we propose a novel Vision-Language Preference learning framework that learns a vision-language preference model to provide preference feedback for embodied manipulation tasks.

we propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL) that enables humanoid robots to traverse extreme terrains, particularly narrow pathways and unexpected obstacles, using only proprioception.

We decompose the reward value in RLHF into two independent components that consists prompt-free reward and prompt-related reward, and propose a new reward learning algorithm by prioritizing data samples based on their prompt-free reward values.

We propose Adversarial Locomotion and Motion Imitation (ALMI) for humanoid robots, which serves as a novel framework for loco-manipulation tasks, enabling adversarial policy learning between upper and lower body.